NDLI: Fall-prediction algorithm using a neural network for safety enhancement of elderly

Content Provider	IEEE Xplore Digital Library
Author	Shih-Hung Yang Wenlong Zhang Yizou Wang Tomizuka, M.
Copyright Year	2013
Description	Author affiliation: Dept. of Mech. & Comput. Aided Eng., Feng Chia Univ., Taichung, Taiwan (Shih-Hung Yang) \|\| Dept. of Mech. Eng., Univ. of California, Berkeley, Berkeley, CA, USA (Wenlong Zhang; Yizou Wang; Tomizuka, M.)
Abstract	Among the elderly, falls are a well-known safety hazard, often resulting in major injury, hospitalization and death. To reduce the injuries caused by falls, it is first necessary to predict a fall as early as possible and then to provide protection for the person who is falling. This paper proposes a fall-prediction algorithm (FPA) that can predict whether the person will fall within one-walking-step. The fall prediction is different from the fall detection, and it is intended to predict a fall before it occurs and provide sufficient time to enable a safety mechanism. The proposed FPA adopts a neural network to perform prediction in which the inputs are accelerations and angular rates of upper trunk and the output presents fall or no fall. A wearable inertial sensor package with a triple axis accelerometer and a triple axis gyroscope is developed to measure the required motion data. Five subjects were asked to wear the inertial sensor package and perform a number of simulated falls. The experimental results show that the FPA could predict a fall 0.4 seconds prior to the beginning of the fall. The time interval is sufficient to inflate an airbag covering the head, trunk, and hip, an intervention that would reduce fall-related injuries among older people.
Starting Page	245
Ending Page	249
File Size	926926
Page Count	5
File Format	PDF
e-ISBN	9781479923830
DOI	10.1109/CACS.2013.6734140
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-02
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Legged locomotion Artificial neural networks Safety Acceleration Injuries Testing
Content Type	Text
Resource Type	Article

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